Simulation-based Optimization Approach Research Articles

Thermal performance optimization of a novel integrated photovoltaic thermal collector system

The construction industry is actively seeking energy-efficient solutions to reduce reliance on fossil fuels. Building-integrated photovoltaic thermal (PV-T) systems, combining electricity generation with solar heat utilization, have emerged as a promising technology. However, conventional PV-T systems often face limitations in achieving optimal PV panel angles for maximum solar gain while maintaining architectural aesthetics, especially when integrated into building facades. This study investigates the thermal performance of a novel photovoltaic thermal collector (PV-TC) system specifically designed to address these challenges. This system, intended for installation on south-facing building facades, allows for flexible PV panel angling design to optimize solar energy capture without compromising the building's aesthetic appeal. We employ a simulation-based optimization approach using EnergyPlus, Radiance, and a genetic algorithm to determine the optimal design parameters for minimizing annual energy use intensity (EUI) in Nanjing, China's climate. Our results demonstrate that a fan rate of 288 m³/h, a winter fan setpoint of 21 °C, a summer fan setpoint of 25 °C, and a PV panel angle of 41° achieve the lowest EUI of 92.33 kWh/m2. This optimized configuration effectively balances electricity generation with solar heat utilization, minimizing reliance on conventional HVAC systems. We further analyzed individual parameter influences, demonstrating that increasing fan rate consistently improves heat transfer efficiency, while optimal PV angle and fan setpoint temperature are crucial for maximizing solar heat utilization and nighttime cooling, respectively. By comparison it was found that the installation of the PV-TC system reduces the annual EUI by 22.7 %. These findings provide valuable insights for designing and operating high-performance PV-TC systems that address both energy efficiency and architectural integration concerns in buildings.

Design by Optimization on the Nozzle and the Stator Blades of a Rim-Driven Pumpjet

The design of the stator and nozzle of a rim-driven pumpjet thruster (RDPJ) is addressed through a simulation-based design optimization approach built on a parametric description of the main geometrical characteristics of the system, a RANS solver with actuator disk model, and a genetic algorithm. As the propeller blades’ geometry is fixed, the rotor/stator (RDPJ-R/S) configuration is considered for the optimal design from a multi-objective optimization process aimed at minimizing the resistance keeping the cavitation inception index at the lowest possible value. Steady-state (moving reference frame plus mixing plane interface) and unsteady simulations (sliding meshes) with fully resolved rotor geometry were finally carried out on six selected optimal geometries to validate the optimization process and the performance improvements provided by the RDPJ configuration when compared with the original rim-driven thruster (RDT).

A simulation-based optimization approach for the recharging scheduling problem of electric buses

This study proposes a simulation-based optimization approach to address the recharging scheduling problem of electric buses to minimize charging waiting time. Poor scheduling could lead to longer waiting times and potentially affect operation schedules regarding time and service quality. This study addresses a simulation-based optimization framework to evaluate various performance metrics during electric bus service, including waiting times, charging costs, and the utilization of charging piles. In this study, we propose a hybrid approach, simplified swarm optimization (SSO), which is an evolutionary algorithm with a backtracking (BT) mechanism and dynamic charging in a simulation framework. Based on the dynamic charging, SSO is used to determine the additional charging in terms of battery capacities, and a BT mechanism is employed to enhance algorithm efficiency and achieve breakthroughs in solution quality. A case study from Taiwan with 43 generated datasets was conducted in deterministic and stochastic situations to compare the effectiveness and efficiency among three charging rules (i.e., full charging rule, flexible charging rule, dynamic charging rule) and two algorithms (i.e., particle swarm optimization and SSO) The results indicate the superior performance in all scenarios by using a statistical test, which offers effective decision support for bus operators’ electric bus recharging scheduling.

Forest management with fire simulation

In this work, we address a forest management problem for timber production with fire concerns, employing a novel simulation-based optimization approach wherein forest management is iteratively guided by the feedback from fire spread simulations.The forest management problem involves selecting an alternative prescription for each stand, subject to various restrictions (e.g. bounds on ecosystems services), to maximize the net present value. For each stand, prescriptions involve projecting forest conditions and outcomes using species-specific growth and yield models, combined with different fuel treatment scenarios. In each iteration, the optimization problem is solved. Fire travel times between adjacent points in a grid representing the forest are calculated, based on fuel models associated with the selected prescriptions and other conditions as wind and slopes. Fire spread is simulated for all potential ignitions. Fire paths with a rate of spread greater than a given threshold are identified and constraints are added to the forestry problem to exclude their associated prescriptions to be jointly selected. This problem is re-optimized and the process is repeated until there are no such paths.We describe computational experiments in a Portuguese forest showing how trade-offs between the net present value and the maximum fire rate of spread can be obtained. When too restrictive conditions are imposed on fire, the approach suggests a set of stands to become fire breaks. We also conducted experiments to demonstrate how the impact of the forest surroundings, as well as bounds on ecosystem services, can be evaluated with respect to these trade-offs.

Many-Objective Simulation Optimization for Camp Location Problems in Humanitarian Logistics

Article Many-Objective Simulation Optimization for Camp Location Problems in Humanitarian Logistics Yani Xue 1,*, Miqing Li 2, Hamid Arabnejad 1, Diana Suleimenova 1, Alireza Jahani 1, Bernhard C. Geiger 3, Freek Boesjes 4, Anastasia Anagnostou 1, Simon J.E. Taylor 1, Xiaohui Liu 1, and Derek Groen 1,* 1 Department of Computer Science, Brunel University London, Uxbridge, United Kingdom 2 School of Computer Science, University of Birmingham, Birmingham, United Kingdom 3 Know-Center GmbH, Graz, Austria 4 Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands * Correspondence: Yani Xue (Yani.Xue3@brunel.ac.uk); Derek Groen (Derek.Groen@brunel.ac.uk) Received: 10 March 2024 Accepted: 19 August 2024 Published: 26 September 2024 Abstract: Humanitarian organizations face a rising number of people fleeing violence or persecution, people who need their protection and support. When this support is given in the right locations, it can be timely, effective and cost-efficient. Successful refugee settlement planning not only considers the support needs of displaced people, but also local environmental conditions and available resources for ensuring survival and health. It is indeed very challenging to find optimal locations for establishing a new refugee camp that satisfy all these objectives. In this paper, we present a novel formulation of the facility location problem with a simulation-based evolutionary many-objective optimization approach to address this problem. We show how this approach, applied to migration simulations, can inform camp selection decisions by demonstrating it for a recent conflict in South Sudan. Our approach may be applicable to diverse humanitarian contexts, and the experimental results have shown it is capable of providing a set of solutions that effectively balance up to five objectives.

Data Envelopment Analysis-based Scenario Selection for Sequencing Pattern in a Simulated Robotic Cell

In this study, the performance of suggested scenarios for part input sequences in a 3-machine robotic cell producing different parts is determined through the application of data envelopment analysis (DEA) and the Banker–Charnes–Cooper model. A single gripper robot supports the manufacturing process by loading and unloading products and moving them inside the system. This study addresses random machine failures and repairs to minimize cycle time based on two robot move cycles in a three-machine robotic cell and overall production costs. Here, simulation assists in the modeling of uncertainty and a simulation-based optimization approach is applied to find the best scenarios for sequencing patterns in the cell through several numerical examples using DEA. The results displayed that, efficient scenarios satisfying minimum time and cost, are those, in which the percentages of operations assigned to the machines are close to each other. This enables decision-makers in manufacturing systems to make precise selections of the optimal part sequencing pattern with the lowest production cost and cycle time for robotic cells.

Redundancy allocation problem in repairable systems with variegated components: a simulation-based optimization approach

The redundancy allocation problem (RAP) is one of the most commonly used approaches for improving the reliability of systems. In previous studies, to simplify the problem model and mathematical solution methods, some real conditions were not emphasized in analysing the issue of RAP. But the present study, while considering these conditions, enables considering multiple goals, analysing repairable systems and examining the failure rate and repair rate appropriate to the type of components. Two goals of this purpose are maximizing the Mean Time To First Failure (MTTFF) and minimizing the total cost of the system, considering the weight and volume limitations and number of plugin components. To solve the problem model four Multi-objective meta-heuristic algorithms have been used: NSGA-II, MOPSO, PESA-II and MALO. In addition, the simulation method is used to estimate the first objective function of the problem.

Design optimisation of roadside safety roller barriers

Safety roller barrier (SRB) is an innovative road safety device that utilises foam rollers to absorb and convert shock energy into rotational energy, showcasing its remarkable ability to minimise the risk and severity of casualties in road accidents. In this study, a simulation-based optimisation approach was employed to obtain the optimal design for the SRB. Quasi-static and dynamic mechanical properties of the construction materials were experimentally obtained. SRB finite element (FE) models were then created based on installed systems in multiple Australian cities. The model was validated using full-scale TL4 crash tests according to the specifications outlined in the Manual for Assessment of Safety Hardware (MASH). Subsequently, a parametric study was conducted to explore the influence of design parameters such as post spacing, rail height, and roller number. Finally, a parallel Bayesian optimisation algorithm was applied to re-design the SRB system with enhanced performance and reduced cost. This optimisation process yielded several sets of optimal parameters, paving the way for a more effective and economically viable SRB design.

Optimal sizing and energy management strategy for an office building with photovoltaic, heat pump, thermal tank, and electric vehicle under time-of-use tariff

The increasing demand for sustainable energy solutions is driving the integration of various renewable energy technologies. Integrating electric vehicle batteries, photovoltaics, heat pumps, and thermal tanks into building energy systems offers a promising approach to enhancing energy flexibility and efficiency. However, research on such integrated energy systems is still in its infancy. Optimizing the configuration and operation of the system may lead to significant cost savings and improved energy management. A coupled model and simulation-based optimization approach are used to optimize the thermal tank capacity and key operational parameters to minimize the annual total cost. The results show that the integrated energy system reduces the annual total cost by 36.35% and monthly electricity bills by up to 69.26% compared to the base system. As for energy flexibility, only 13.91% of electricity demand is supplied by the grid during peak periods, and the monthly load coefficient, load cover ratio, and photovoltaic self-consumption ratio of the integrated energy system are significantly improved. This study demonstrates the economic and operational benefits of integrating various renewable energy technologies into building energy systems and provides new insights into their design and optimization.

Viable Vitreous Grafts of Whole Porcine Menisci for Transplant in the Knee and Temporomandibular Joints.

The shortage of suitable donor meniscus grafts from the knee and temporomandibular joint (TMJ) impedes treatments for millions of patients. Vitrification offers a promising solution by transitioning these tissues into a vitreous state at cryogenic temperatures, protecting them from ice crystal damage using high concentrations of cryoprotectant agents (CPAs). However, vitrification's success is hindered for larger tissues (>3 mL) due to challenges in CPA penetration. Dense avascular meniscus tissues require extended CPA exposure for adequate penetration; however, prolonged exposure becomes cytotoxic. Balancing penetration and reducing cell toxicity is required. To overcome this hurdle, a simulation-based optimization approach is developed by combining computational modeling with microcomputed tomography (µCT) imaging to predict 3D CPA distributions within tissues over time accurately. This approach minimizes CPA exposure time, resulting in 85% viability in 4-mL meniscal specimens, 70% in 10-mL whole knee menisci, and 85% in 15-mL whole TMJ menisci (i.e., TMJ disc) post-vitrification, outperforming slow-freezing methods (20%-40%), in a pig model. The extracellular matrix (ECM) structure and biomechanical strength of vitreous tissues remain largely intact. Vitreous meniscus grafts demonstrate clinical-level viability (≥70%), closely resembling the material properties of native tissues, with long-term availability for transplantation. The enhanced vitrification technology opens new possibilities for other avascular grafts.

Multi-objective optimisation of a logistics area in the context of factory layout planning

ABSTRACT The manufacturing factory layout planning process is commonly supported by the use of digital tools, enabling creation and testing of potential layouts before being realised in the real world. The process relies on engineers’ experience and inputs from several cross-disciplinary functions, meaning that it is subjective, iterative and prone to errors and delays. To address this issue, new tools and methods are needed to make the planning process more objective, efficient and able to consider multiple objectives simultaneously. This work suggests and demonstrates a simulation-based multi-objective optimisation approach that assists the generation and assessment of factory layout proposals, where objectives and constraints related to safety regulations, workers’ well-being and walking distance are considered simultaneously. The paper illustrates how layout planning for a logistics area can become a cross-disciplinary and transparent activity, while being automated to a higher degree, providing objective results to facilitate informed decision-making.

Digital twin-based reinforcement learning framework: application to autonomous mobile robot dispatching

ABSTRACT This paper proposes a new framework for embedding an Intelligent Digital Twin (DT) in a production system with the objective of achieving more efficient real-time production planning and control. For that purpose, the Intelligence Layer is based on Reinforcement Leaning (RL) and Deep RL (DRL) algorithms. The use of this control instead of parametric simulation-based optimization approach allows to benefit from the separation between the training and execution phase. To ensure consistency and reusability, this work presents a standardized framework, based on a formal methodology, that specifies how the various components of the DT-based RL architecture interact over time to achieve essential real-time concurrency and synchronization aspects. Experiments are conducted in a small-scale production system where material handling operations are performed by an Autonomous Mobile Robot (AMR) in an Industry 4.0 Laboratory. Results showed how synchronized state updates between the Physical and Cyber World are used within the Decision Layer to ensure real-time response for the AMR dispatching requests. Finally, to deal with continuous and high-dimensional state spaces, the Deep Q-Network is implemented. The findings of an extensive computational study reveal that implementing the DT-based DRL solution leads to improved efficiency and robustness when compared to conventional dispatching rules.

Exploring Heuristic and Optimization Approaches for Elevator Group Control Systems

This paper undertakes an examination of elevator car dispatching methods in response to hall calls. Firstly, our study focuses on the establishment of a representation of an elevator group control system as a finite-state machine to understand the dynamics of elevator group control. Secondly, two primary heuristics are explored, with the first advocating directional continuity unless the highest or lowest floor has been reached, while the second permits direction change upon completing the final call, regardless of floor extremes. Identified inefficiencies in these heuristic solutions lead us to explore enhanced alternatives. Consequently, we delve into genetic algorithm (GA) and simulated annealing (SA) methodologies. Our focus initially centers on devising solution representations and determining fitness evaluations for both approaches. We employ a simulation-based optimization approach to identify the optimal parameter values for both simulated annealing and genetic algorithms. A subsequent comparative analysis is conducted to ascertain the most effective approach among these diverse solutions. A comparative analysis reveals that the GA-based approach significantly outperforms both existing heuristics and the SA-based method in minimizing average passenger waiting time at the cost of longer computational time.

A social network and simulation-based optimization approach for manufacturing resilience in a distributed manufacturing system

Manufacturing systems in the distributed environment are becoming increasingly complex and challenging to manage as a result of contemporary client requirements, such as mass individualization, highly customized, and sustainable products. However, loss of production caused by a multitude of risks and disruptions, in addition to the aforementioned issues, necessitates the establishment of a resilient system. Rendering the ability of a system to respond and recovery from these disruptions to achieve manufacturing resilience need effective production planning and scheduling strategies. In this paper, we define Time to Survive (TTS) and Time to Recovery (TTR) and throughput loss as a metrics to aid the robust and recovery phases of developing a resilient distributed manufacturing system. A social network analysis based simulation approach is developed to assess the performance of pre- and post-disruption scenarios. Specifically, we investigate the complex distributed manufacturing system by disrupting the identified critical machines/enterprises and its effects on system performance. Numerical case studies are being undertaken to study the effects of disrupted firms on distributed manufacturing system performance by using simulation and optimization approach, as well as how rescheduling of process planning and scheduling affects performance measures like as system configurations, robustness, and resilience.

Enhancing the effectiveness of bioaerosol disinfection in indoor environments by optimizing far-UVC lamp locations based on Markov chain model

Far-ultraviolet C (far-UVC) light is an effective and safe disinfection method for bioaerosol control in occupied indoor environments. The installation location of a far-UVC lamp strongly influences the spatial distribution of far-UVC irradiance, and thus the effectiveness of bioaerosol disinfection. To assist the design process, this study developed a fast prediction approach based on the Markov chain model for optimizing the installation locations of far-UVC lamps in order to enhance the disinfection effectiveness for indoor bioaerosol control. Experiments were conducted in an environmental chamber to validate the proposed simulation-based optimization approach. The results show that the proposed method can correctly predict the disinfection efficiency when compared with experimental data, and optimizing the installation location of the far-UVC lamp increased the disinfection efficiency by 54 % compared with the worst location. As an application, the validated method was then used to design the installation location of a far-UVC lamp in a real conference room. The results show that installing the far-UVC lamp in the optimal location can increase the disinfection efficiency by 48 % compared with the worst installation location. Therefore, optimizing the far-UVC lamp location using the proposed Markov chain model can enhance the effectiveness of bioaerosol disinfection in indoor environments.

On-demand last-mile distribution network design with omnichannel inventory

E-commerce delivery deadlines are getting increasingly tight, driven by a growing ‘I-want-it-now’ instant gratification mindset of consumers and the desire of online and omnichannel retailers to capitalize on the growth of on-demand e-commerce. On-demand deliveries with delivery deadlines as tight as one or two hours force companies to rethink their last-mile distribution network, since tight delivery deadlines require decentralization of order picking and inventory holding to ensure close proximity to consumers. This fundamentally changes the strategic design process of last-mile distribution networks. We study the impact of incorporating inventory order-up-to level decisions into the strategic design process of last-mile distribution networks with tight delivery deadlines. We develop an approximate inventory model by including an estimate of the cost of late delivery and additional transportation due to local stock-outs in a newsvendor formulation. Such local stock-outs require an order to be delivered from a more distant facility, which may lead to late delivery and additional transportation cost. We integrate our approximate inventory model and a location–allocation mixed-integer program that determines optimal facility locations, associated order-up-to inventory levels, and fleet composition, into a metamodel simulation-based optimization approach. Our numerical analyses demonstrate that pooling the additional online inventory with brick-and-mortar (B&M) inventories leads to cannibalization by the B&M network and higher B&M service levels. However, the pooling benefits to the online network outweigh the cost of inventory cannibalization. Furthermore, we show under which circumstances omnichannel retailers may have an incentive to consolidate online inventory in specific B&M facilities.

Multi-objective optimization of sandwich structures for reinforcing composite fuselages

This study introduces a simulation-based optimization approach that combines a multi-objective genetic algorithm and the finite element method to design sandwich structures to reinforce composite fuselages. The sandwich structure has been parametrized with a set of mixed integer-continuous variables representing the polymer core thickness, the number and weight of laminated fiberglass layers, and size and position of the structure. Automatic scripting builds the geometry of the structure, then mounts it in the fuselage, and finally creates a conformal mesh. During the optimization, each reinforced fuselage is subjected to several load cases specified by the CS-22 EASA design standard, determining the worst load condition. Two objectives were considered: increasing the reinforced fuselage’s minimum fiber stress safety factor for improved safety and reducing the number of layers of the sandwich structure to minimize manufacturing costs. Structural constraints were the mass of the sandwich structure and buckling and flexo-torsional deformation of the reinforced fuselage. Results show an efficient material arrangement due to the reduced number of layers in the sandwich structure and a stress safety factor surpassing that set by the standard. Finally, the reinforcement of a glider fuselage to incorporate a retractable electric propulsion system is presented as a real-world industrial application.

Techno-economic analysis and process optimization of a PET chemical recycling process based on Bayesian optimization

Chemical recycling has gained interest as a promising option to reduce the consumption of fossil feedstocks and relieve environmental issues concerning plastic waste. In this study, we present a preliminary techno-economic analysis of a process design for chemical recycling of polyethylene terephthalate (PET). A conceptual base case design was developed for a continuous catalytic PET depolymerization process. The process design was optimized to minimize total annualized cost (TAC) based on a simulation-based Bayesian optimization approach. A novel interface was utilized to manipulate the process variables, and to retrieve simulation and economic evaluation results. Through five trials of optimization, the process design was improved by about 4.2∼4.4 % in terms of TAC. Sensitivity analyses investigating the effect of process parameters and price variables on the minimum selling price of main product, p-xylene, are presented. The feedstock PET flake price was identified as the most critical factor affecting the economic viability of the process.

Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty

The ever-increasing demand for intercity travel, as well as competition among all modes of transportation, is an unavoidable reality that today’s urban rail transit system must deal with. To meet this problem, urban railway companies must try to make better use of their existing plans and resources. Analytical approaches or simulation modeling can be used to develop or change a rail schedule to reflect the appropriate passenger demand. However, in the case of complex railway networks with several interlocking zones, analytical methods frequently have drawbacks. The goal of this article is to create a new simulation-based optimization model for the Bangkok railway system that takes into account the real assumptions and requirements in the railway system, such as uncertainty. The common particle swarm optimization (PSO) technique is combined with the developed simulation model to optimize the headways for each period in each day. Two different objective functions are incorporated into the models to consider both customer satisfaction by reducing the average waiting time and railway management satisfaction by reducing needed energy usage (e.g., reducing operating trains). The results obtained using a real dataset from the Bangkok railway system demonstrate that the simulation-based optimization approach for robust train service timetable scheduling, which incorporates both passenger waiting times and the number of operating trains as equally important objectives, successfully achieved an average waiting time of 11.02 min (with a standard deviation of 1.65 min) across all time intervals.

Probability-based visual comfort assessment and optimization in national fitness halls under sports behavior uncertainty

Sports behavior uncertainty is a main cause of mismatch between simulated and actual visual comfort in sports spaces, which has not been properly addressed in existing studies. This study proposes a multi-phase and multi-objective optimization approach to improve visual comfort in national fitness halls by better considering sports behavior uncertainty. Specifically, a surrogate-based multi-objective optimization workflow is developed for fast optimization of visual comfort. Then, a probabilistic behavior model is created to predict player's occupancy probability in all possible view scenes during sports, and based on which the probabilistic visual comfort is introduced to assess player's realistic visual comfort taking account of the occupancy probability. The sport of tennis in a parametric national fitness hall is selected as the case study scenario. Results indicated that the proposed response surface model outperformed artificial neural network model in predicting visual comfort, while this approach saved 35.43% of total computational time versus the traditional simulation-based multi-objective optimization approach in this case study; 296 Pareto solutions out of 1758 design alternatives were quickly identified, among which the final optimum solution indicated an approximately 200% better visual comfort against the worst one; Sports behavior uncertainty was found to affect player's visual comfort assessment in tennis sports, presenting variances on different court locations under two court layouts. This study contributes new insights of probabilistic visual comfort assessment under sports scenarios and explores the potential of court location and layout arrangement for multi-purpose usage consideration in national fitness halls.